Apropos of “Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals”

Oggie Arandelovic

doi:10.1007/s43681-022-00255-4

Apropos of “Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals”

Oggie Arandelovic^*

^*Corresponding author for this work

School of Computer Science

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors’ analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its appeal in large part relying on the extant consensus in the community.

Original language	English
Pages (from-to)	1021-1023
Number of pages	3
Journal	AI & Ethics Journal
Volume	3
Early online date	9 Jan 2023
DOIs	https://doi.org/10.1007/s43681-022-00255-4
Publication status	Published - 1 Aug 2023

Keywords

Fairness
Value of life
Exploitation
Computer vision
Machine learning

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title = "Apropos of “Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals”",

abstract = "The present comment concerns a recent AI \& Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors{\textquoteright} analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its appeal in large part relying on the extant consensus in the community.",

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N2 - The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors’ analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its appeal in large part relying on the extant consensus in the community.

AB - The present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors’ analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its appeal in large part relying on the extant consensus in the community.

KW - Fairness

KW - Value of life

KW - Exploitation

KW - Computer vision

KW - Machine learning

U2 - 10.1007/s43681-022-00255-4

DO - 10.1007/s43681-022-00255-4

M3 - Comment/debate

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EP - 1023

JO - AI & Ethics Journal

JF - AI & Ethics Journal

ER -

Apropos of “Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals”

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